
GAMs enable NAM-NC to explain NC results after training without additional model-agnostic methods [1]}.

Multivariate NC is supported, and each NC is influenced by cross-correlating other time series, including auto-correlations.

Two-parameter sharing structures reduce NAM-NC’s complexity, and with reduced parameters, its performance is maintained.

State-of-the-art performance is maintained for time-series NC tasks using real datasets.

